0. Data Preparation

0.1. Vaccination in ukraine data aggregation.

0.2. Covid statistics in ukraine data aggregation.

0.3. Ukraine map preparation.

Now, we are ready to start analyzing existing data and create visualization. However, for that reason we firstly need to set the ground questions that we want to analyze in this work.

1. See the general statictics of covid spread in Ukraine (for the last 6 month).
2. Discover the vaccination tendends in Ukraine (for the last 6 month).

1. Covid statistics analysis.

1.1 General statictics.

This visualization shows the newest daily updated destribution of covid case.

Alterntive approach for visualizing this data would be to use the standart barplot or pieplot (for some reasons isn't working), either of these solutions will work good in this situation.

This viualization was made to compare the dynamic of covid cases registration through last 6 month, and show a new covid wave that happened this autumn.

Alternative approache was to use bar plot with normalization to simplify the plot a bit, by this areaplot hytlight better the new covid way effect.

1.2. Confirmed cases statistics comparisons by regions.

This viualization is designed to show the increase of new confirmed covid case and how quickly they increase throught the last 6 month.

However, the main disadvantage of this approach is that the confirmed case are not normalized by the region population and becaue of that bigger and pore populated regions gets more attantion on the map.

The second problem with this visualization is pour UI of standard HTML range that are used by Altair - the right preview of range value is bad (since the only way that worked for me is using timestamps) and the range lack of the stage for different month which creates a bad UX. Alternative approach here would be to use the radio select buttons, but that is not quite the expereince I would like to achive.

1.3. Covid cases status proportions comparisons by regions

This visualization is a bit similar to the one used in 1.1 section, but with the difference that now all plots are normalized. That's done for vanishing the difference in regions population, but showing that for all the region the increase in the new covid case can be seen in the autumn months.

Alternatively, if we decide to how only the trends in the existing confirmed covid case, then we can use standart line plot and show only existing covid case, but again due to the lack of cities populations the visialization results would be unfair to the smaller cities.

2. Vaccination statistics analysis.

This visualization tries to show the increase of the vaccination trend in the autumn (which is most likely due to new quarantine prohibitions and the increase of new covid case increase shown in the previous sections). Also, this visualization shows a bit unexpected outlier region - Zakarpatska - which is the only region when the 'Johnson & Johnson' vaccination was used.